Literature DB >> 33682746

Statistical analysis of age-related skin parameters.

Hong Meng1, Weixuan Lin1, Yinmao Dong1, Li Li1, Fan Yi1, Qingyang Meng2, Yue Li3, Yifan He1.   

Abstract

BACKGROUND: Due to the increasing interest in human anti-aging, demand for a higher quality of life, and technological advancement, the development of anti-aging skincare has great market prospects. Most cosmetic companies develop products driven by the market or focus on the mechanism of action of substances and the behavior of skin; however, little research utilizes skin parameters and large data methodology to develop skincare products.
OBJECTIVE: To instruct consumers to purchase skincare products and to guide skincare research toward the development of customer-targeted products.
METHODS: A total of 815 Chinese subjects (83 male; 732 female) from five different cities were included. We measured 14 indices in each subject, including moisture, transepidermal water loss (TEWL), and sebum levels. We performed multiple regression analysis to understand the relationship between skin indices and aging; a novel approach is shown using the R software.
RESULTS: The exact age at which changes in each skin index occurred could be demonstrated by this method of analysis: 39, 38, 48, 46, and 56 years old with respect to the L value, Melanin, Rt, Rm, and Rz, respectively.
CONCLUSION: With the use of statistical analysis, consumers can be more efficiently targeted and choose suitable products considering particular skin parameter changing points; thus, skincare companies will not only meet customer requirements but also better control budgets.

Entities:  

Keywords:  Multiple regression analysis; R software; changing point; skin aging; skin parameters

Year:  2021        PMID: 33682746     DOI: 10.3233/THC-218007

Source DB:  PubMed          Journal:  Technol Health Care        ISSN: 0928-7329            Impact factor:   1.285


  1 in total

1.  Developing Criteria for Asian Facial Skin Health Status Based on a Non-Invasive Skin Test: The Delphi Process.

Authors:  Nan Li; Mengmeng Zhao; Xiaoxiao Yang; Yifan He; Fan Yi
Journal:  Clin Cosmet Investig Dermatol       Date:  2022-03-05
  1 in total

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